This disclosure relates to processing electronic signals associated with a payment network to determine a financial performance of a merchant, and more specifically to determining a level of risk that the merchant will commit insurance fraud based on data included in the signals.
Research has shown that at least some people experiencing financial difficulty commit insurance fraud in an attempt to lessen their financial burdens. For example, a person may intentionally destroy property that the person has an insurance policy on and attempt to collect money from the insurance company for the loss of the property. Such fraud occurs with respect to fire insurance, vehicle insurance, and other types of insurance. For another example, a person operating a business at a particular commercial property may experience a downturn in revenue and become financially distressed. In some cases, the person becomes so financially distressed that the person intentionally sets fire to the commercial property in an attempt to collect from an insurer where the person holds a fire insurance policy for the property. Similarly, a person may destroy their vehicle and report it missing, to collect on auto insurance.
Alternatively, a distressed merchant may take other unlawful actions to lessen their financial burden. For example, a merchant may submit payment authorization requests for fake purchases using stolen payment card information in an attempt to increase their revenue.
Accordingly, it would be beneficial to identify signs of impending insurance fraud before the fraud actually occurs, and notify insurance companies of the risk of fraud before it occurs so that the insurance companies can react accordingly. Additionally, even after the fraud occurs, it would be helpful to identify a likelihood of insurance fraud by the merchant, in order to estimate the likelihood that the merchant was involved in fraud.